---
title: Predict Text Sentiment with Hugging Face and MindsDB
sidebarTitle: Predict Text Sentiment
---

In this tutorial, we'll use a model from the Hugging Fae hub to predict text sentiment.

## Connect a database

We start by connecting a demo database using the `CREATE DATABASE` statement.

```sql
CREATE DATABASE example_db
WITH ENGINE = "postgres",
PARAMETERS = {
    "user": "demo_user",
    "password": "demo_password",
    "host": "3.220.66.106",
    "port": "5432",
    "database": "demo",
    "schema": "demo_data"
    };
```

Let’s preview the `user_comments` table.

```sql
SELECT *
FROM example_db.user_comments;
```

## Create a Hugging Face model

Our Hugging Face integration automatically manages downloading and deploying of pre-trained transformers from Hugging Face's hub. For example, we can download a transformer which has been trained to classify the sentiment of text.

```sql
CREATE MODEL sentiment_classifier
PREDICT sentiment
USING
   engine='huggingface',
   model_name= 'cardiffnlp/twitter-roberta-base-sentiment',
   task='text-classification',
   input_column = 'comment',
   labels=['negative','neutral','positive'];
```

To create a model in MindsDB, we use the `CREATE MODEL` statement. Next, we define the target column using the PREDICT clause. Finally, we specify all required parameters in the `USING` clause.

Once the above query is executed, we can check the status of the creation process:

```sql
DESCRIBE sentiment_classifier;
```

## Make predictions

Once the status is complete, the behavior is the same as with any other AI table you can query it and provide input data in the `WHERE` clause, like this:

```sql
SELECT * FROM sentiment_classifier
WHERE comment='It is really easy to do NLP with MindsDB';
```

The above query should predict the comment as ‘positive’.

We can also make batch predictions by joining the input data table with the model, like this:

```sql
SELECT input.comment, model.sentiment
FROM example_db.user_comments AS input
JOIN sentiment_classifier AS model;
```
